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Record W3213013271 · doi:10.15376/biores.17.1.243-254

Comparative effect of selected anti-mildew agents on bamboo bundles

2021· article· en· W3213013271 on OpenAlexaff
Zhenzeng Wu, John Tosin Aladejana, Daobang Huang, Gong Xinhuai, Shuqiong Liu, Xiaodong Wang, Yongqun Xie

Bibliographic record

VenueBioResources · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBamboo properties and applications
Canadian institutionsUniversité Laval
FundersWuyi University
KeywordsBoric acidMildewBambooPhosphoric acidPhosphateSodium silicateMaterials scienceNuclear chemistryBoronChemistryMetallurgyComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Nutrient-rich raw bamboo materials can be infected by mildew when exposed to water or high humid environments. This not only affects the appearance of bamboo products, but it also contributes to respiratory diseases. Herein, four anti-mildew agents, i.e., boric acid, copper sulphate, alumina phosphate sol, and alumina silicate sol, were used to evaluate their anti-mildew performances. The results showed that the adequate anti-mold concentrations of boron and copper were 2% and 0.7%, respectively. The optimum mass ratio of aluminum phosphate sol and silicone aluminum sol were 1 to 1 (2% phosphoric acid addition) and 10 to 1 (aluminum salt addition was 1.5%). There were significant differences in the prevention and treatment effects of different mold inhibitors on mold and discoloration bacteria. The efficacies order of the anti-mildew property was as follows: copper sulphate > alumina silicate sol > boric acid > alumina phosphate sol. In addition, the order for stain fungi resistance was: boric acid > alumina phosphate sol > alumina silicate sol > copper sulphate. The selected anti-mildew agents showed promising application requirements as an active ingredient in bamboo preservative systems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.458
Threshold uncertainty score0.353

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.031
GPT teacher head0.249
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2021
Admission routes1
Has abstractyes

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